SARFish / README.md
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Updated SARFish dataset card.
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metadata
license: apache-2.0
task_categories:
  - object-detection
  - image-classification
tags:
  - SARFish
  - Illegal Fishing
  - Computer Vision
  - Complex-Valued
  - Synthetic Aperture Radar
pretty_name: SARFish Dataset
size_categories:
  - n<1K

SARFish is a Synthetic Aperture Radar (SAR) imagery dataset for the purpose of training, validating and testing supervised machine learning models on the tasks of ship detection, classification, and length regression. The SARFish dataset builds on the excellent work of the xView3-SAR dataset (2021) and consists of two parts:

  1. Data - Extends the xView3-SAR dataset to include Single Look Complex (SLC) as well as Ground Range Detected (GRD) imagery data taken directly from the European Space Agency (ESA) Copernicus Programme Open Access Hub Website.
  2. Labels - Derives labels from the xView3-SAR dataset providing maritime object location, vessel classification and vessel length information.

Quick Links

The following are links to the Kaggle competitions for each of the tracks of the SARFish challenge along with the SARFish dataset and GitHub repo:

The GitHub repo describes how to:

  • Download the dataset.
  • Run the SARFish_demo jupyter notebook.
  • Load imagery products and groundtruth labels,
  • Train and evaluate a reference/baseline model using the dataset.

Dataset summary - What does the SARFish dataset consist of?

The following table summarises the sizes of the full size and sample SARFish dataset.

dataset coincident GRD, SLC products compressed (GB) uncompressed (GB)
SARFishSample 1 4.3 8.2
SARFish 753 3293 6468

The following table summarises the partitions of the dataset:

Partition Coincident products Labels Provided Unique maritime object labels
SLC GRD
train 553 True 63071 64054
validation 50 True 18906 19222
public 150 False 58744 60008
Total 140721 143284

How to access the SARFish dataset

The SARFish dataset is available for download at:

Full SARFish dataset

Make sure you have at least enough storage space for the uncompressed dataset.

cd /path/to/large/storage/location

[Create|login] to a huggingface account.

Login to the huggingface command line interface.

huggingface-cli login

Copy the access token in settings/Access Tokens from your huggingface account. Clone the dataset

git lfs install
git clone https://huggingface.co/datasets/ConnorLuckettDSTG/SARFish

SARFish sample dataset

Substitute the final command for the full dataset with the following:

git clone https://huggingface.co/datasets/ConnorLuckettDSTG/SARFishSample

Follow the instructions of the github repo README to check the md5sums of the data and unzip them.

Labels

The SARFish dataset labels are derived from the labels supplied with the xView-3 SAR dataset. The SARFish dataset labels are available for download from the DIU website. Be sure to take into account country restrictions.

Data

SARFish extends the xView3-SAR dataset by providing products from the Sentinel-1 C-band SAR satellite constellation operated by the European Space Agency’s (ESA) Copernicus Programme available on their Open Access Hub Website in both real-valued GRD and complex-valued SLC product types.

The above image shows a condensed summary of the image formation pipeline of the Sentinel-1 products provided by the Sentinel-1 Mission Performance Center. Note that the SLC and GRD products both share a common ancestor.

The above image shows the relationship between the xView3-SAR and SARFish datasets.

Summary table

The following table compares the GRD and SLC products of the SARFish dataset [3][4]

Platform Sentinel-1 (A, B)
Operator European Space Agency (ESA) Sentinel-1 Mission Performance Center
Sensor CBand SAR
Mode Interferometric Wide Swath (IW)
Polarisations VV, VH
Ground range coverage (km) 251.8
Product type SLC GRD
Pixel value Complex Magnitude Detected
Data type Complex Int16 Unsigned Int16
Azimuth pixel spacing (m) 2.3 10
Range pixel spacing (m) 14.1 10

Ground Range Detected (GRD) Products

GRD products consist of two 'detected' imagery products in VH, VV polarisations. The imagery data is stored in GeoTiff format. Also included in the dataset are no_data masks and shoreline files which are used to evaluate 'close-to-shore' maritime object detection tasks.

Single Look Complex (SLC) Products

SARFish Single Look Complex (SLC) example swath 1

SARFish Single Look Complex (SLC) example swath 2

SARFish Single Look Complex (SLC) example swath 3

The figures above show the 'swaths' comprising a SARFish SLC product in VH polarisation with groundtruth maritime object. labels The complex data has been 'detected' [3] by projecting the complex-valued data onto the real numbers for visualisation and displayed on decibel scale where the dynamic range is between 15 and 60 dB. Note that the SLC products have non-square (x, y): 2.3 × 14.1 m pixel spacing. The native format of the data is Complex Int16.

SARFish SLC footprint

The figure above shows the footprint of the first swath of the example SLC product in context. The footprint was plotted using Clyde D'Cruz' "openstreetmap WKT playground".

SARFish SLC VH polarisation ship example

SARFish SLC VV polarisation ship example

The above images show detail of a labelled vessel in a SLC product in both VH (above) and VV (below) polarisations. Note the differences in the speckle and side-lobing artefacts on the vessel between polarisations and the non-square pixel spacing.

Labels

Location labels

The labels denote the image pixel and geographic coordinate location of the maritime object.

field data_type description
detect_lat float latitude of detection in World Geodetic System (WGS) 84 coordinates
detect_lon float longitude of detection in WGS84 coordinates
detect_scene_row int pixel row of scene containing detection
detect_scene_column int pixel column of scene containing detection

Classification Labels

The labels for the maritime object classification are organised in the same hierarchical structure as the xView3-SAR challenge labels:

label_heirarchy:
└── maritime_objects
    └── vessels
        └── fishing_vessels

They are denoted by the following columns in the labels:

field data_type description
is_vessel bool True if detection is a vessel, False otherwise
is_fishing bool True if detection is a fishing vessel, False otherwise

The maritime object categories are labelled using boolean values to the following questions:

  • is the maritime object a vessel?
  • is the vessel a fishing vessel?

The following table shows the combinations of hierarchical classification labels present in the SARFish dataset:

is_vessel is_fishing
False nan
True nan
False
True
nan nan

Vessel Length Labels

The vessel lengths are denoted in the following column in the labels:

field data_type description
vessel_length_m float length of vessel in meters; only provided where available from AIS

Detailed labels summary

field data_type description
partition str: {"train", "validation"} split of the dataset
product_type str: {"GRD", "SLC"} product type of the data
scene_id str unique xView3 scene ID for challenge purposes
detect_id str unique detection ID in the format: {scene_id}_{detect_lat}_{detect_lon}
{product_type}_product_identifier str The Copernicus Sentinel-1 product identifier for the designated product type
detect_lat float latitude of detection in World Geodetic System (WGS) 84 coordinates
detect_lon float longitude of detection in WGS84 coordinates
detect_scene_row int pixel row of scene containing detection
detect_scene_column int pixel column of scene containing detection
top float pixel row of the top left corner of the bounding box, where available
left float pixel column of the top left corner of the bounding box, where available
bottom float pixel row of the bottom right corner of the bounding box, where available
right float pixel column of the bottom right corner of the bounding box, where available
vessel_length_m float length of vessel in meters; only provided where available from AIS
source str: {AIS, AIS/Manual, Manual} source of detection (AIS, manual label, or both)
is_vessel bool True if detection is a vessel, False otherwise
is_fishing bool True if detection is a fishing vessel, False otherwise
global_shoreline_vector_distance_from_shore_km float distance from shore of detection in kilometers as determined using the global shoreline vectors projected into the pixel space of the SARFish products
xView3_shoreline_vector_distance_from_shore_km float distance from shore of detection in kilometers as determined using the xView3-SAR shoreline vectors projected into the pixel space of the SARFish products
confidence str: {HIGH, MEDIUM, LOW} level of confidence for is_vessel and is_fishing labels

Source

The Sentinel-1 GRD and SLC products were downloaded the University of Alaska's Alaska Satellite Facillity (ASF) which operates NASA's Distributed Active Archive Center (DAAC).

[1]. Tri-Tan Cao, Connor Luckett, Jerome Williams, Tristrom Cooke, Ben Yip, Arvind Rajagopalan, and Sebastien Wong. Sarfish: Space-based maritime surveillance using complex synthetic aperture radar imagery. In 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1–8. IEEE, 2022.

[2] xview3-sar: Detecting dark fishing activity using synthetic aperture radar imagery. arXiv:2206.00897v4 [cs.CV], Nov 2022.

[3] M. Bourbigot, H. Johnsen, R. Piantanida, and G. Hajduch, Sentinel-1 Product Definition. Sentinel-1 Mission Performance Centre, 2016. [Online]. Available: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/document-library/-/asset_publisher/1dO7RF5fJMbd/content/sentinel-1-product-definition

[4] S. N. R. Chandra, J. Christopherson, and K. A. Casey, 2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium. US Geological Survey, 2020.